Dynamic Cement Supply Chain Planning Using Integrated Logarithmic Regression and Fuzzy Inference in Indonesia

Abstract

This study introduces an advanced dynamic simulation framework that integrates System Dynamics (SD), Game Theory, and Fuzzy Inference Systems (FIS) to optimize decision-making in cement logistics. The model incorporates critical variables, including demand fluctuations, production capacity constraints, inventory policies, and logistics disruptions, providing a robust tool for predicting system behaviour under diverse conditions. Applied to a real-world case in the Indonesian cement industry, the framework evaluates three scenarios: Optimistic, Moderate, and Pessimistic. In the Optimistic scenario, characterized by a 12% annual demand increase and an 8% capacity expansion, logistics costs decreased by 20%, and order fulfilment improved by 15%. The Moderate scenario, with a 5% demand rise and 3% capacity growth, yielded stable costs and marginal efficiency gains. Conversely, the Pessimistic scenario, marked by stagnant demand (2%) and unchanged capacity, resulted in an 18% increase in logistics costs and a 12% decline in order fulfilment. These findings underscore the effectiveness of dynamic simulation in enhancing logistics strategies, mitigating risks, and strengthening competitiveness within the cement supply chain.